Lfs update & removal

.gitignore

@@ -8,3 +8,6 @@ vprtempo/dataset/winter/
 vprtempo/dataset/event.csv
 vprtempo/output/
 vprtempo/src/__pycache__/
+vprtempo/models/springfall_VPRTempo_IN3136_FN6272_DB500.pth
+vprtempo/models/springfall_VPRTempoQuant_IN3136_FN6272_DB500.pth
+tutorials/mats/1_BasicDemo/

README.md

@@ -126,18 +126,8 @@ Running VPRTempo and VPRTempoQuant is handlded by `main.py`, which can be operat
 * The VPRTempo dependencies have been installed and/or the conda environment has been activated
 
 ### Pretrained models
-We provide two pretrained models, for `VPRTempo` and `VPRTempoQuant`, that have learned a 500 place sequence from two Nordland traversals (Spring & Fall) which can be used to inference with Summer or Winter. To get the pretrained models, please download them:
+We provide two pretrained models, for `VPRTempo` and `VPRTempoQuant`, that have learned a 500 place sequence from two Nordland traversals (Spring & Fall) which can be used to inference with Summer or Winter. To get the pretrained models, please download them [here](https://www.dropbox.com/scl/fi/ysfz7t7ek6h0pslwq9hd4/VPRTempo_pretrained_models.zip?rlkey=thg0rhn0hjsyov6zov63ni11o&st=nvimet71&dl=0).
 
-```console
-# Ensure your directory is set to VPRTempo
-cd ~/VPRTempo
-
-# If not already installed, install Git lfs
-git lfs install
-
-# Download the pretrained models
-git lfs pull
-```
 ### Run the inference network
 The `main.py` script handles running the inference network, there are two options:
 

tutorials/1_BasicDemo.ipynb

@@ -44,24 +44,7 @@
    "id": "66b11853-6e17-4884-ac92-d35d814add42",
    "metadata": {},
    "source": [
-    "Next, we will need to get the pretrained weights for the model. To get them and the other materials for the , we will use [Git Large File Storage](https://git-lfs.com/) to download the required materials. Please ensure you have it downloaded on your local machine prior to running these tutorials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be03b081",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import subprocess\n",
-    "\n",
-    "# Run Git LFS pull command\n",
-    "try:\n",
-    "    result = subprocess.run(\"git lfs pull\", shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n",
-    "    print(\"Git LFS pull successful.\")\n",
-    "except subprocess.CalledProcessError as e:\n",
-    "    print(f\"Git LFS pull failed with error:\\n{e.stderr}\")"
+    "Next, we will need to get the pretrained weights for the model. To get them and the other materials, please download them from [here](https://www.dropbox.com/scl/fi/bxbzm47kxl24x979q5r5s/1_BasicDemo.zip?rlkey=0umij016whwgm11frzlk63v5k&st=hncbx0ld&dl=0). Unzip the files into the `./tutorials/mats/` folder."
    ]
   },
   {

tutorials/mats/README.txt

@@ -0,0 +1,3 @@
+To download materials for BasicDemo, please visit -> https://www.dropbox.com/scl/fi/bxbzm47kxl24x979q5r5s/1_BasicDemo.zip?rlkey=0umij016whwgm11frzlk63v5k&st=52otrem5&dl=0
+
+Extract into the `./tutorials/mats` folder.

vprtempo/models/README.txt

@@ -0,0 +1 @@
+To download pretrained models, please visit -> https://www.dropbox.com/scl/fi/ysfz7t7ek6h0pslwq9hd4/VPRTempo_pretrained_models.zip?rlkey=thg0rhn0hjsyov6zov63ni11o&st=nvimet71&dl=0